The Consilience of Neural and Artificial Reinforcement Learning

author: Peter Dayan, Gatsby Computational Neuroscience Unit, University College London
published: Aug. 23, 2017,   recorded: February 2017,   views: 1637

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Animals that fail to predict or control events associated with rewards and punishments are not long for this world. Reinforcement learning thus offers a body of theory that organizes and motivates a huge wealth of work in psychology and neuroscience. Equally, these latter disciplines provide inspiration for new methods, ideas and problems in the wider field of reinforcement learning. I will discuss this consilience, illustrating the fecundity of the approaches and some of the challenges and opportunities ahead.

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